Convert number strings with commas in pandas DataFrame to float

I have a DataFrame that contains numbers as strings with commas for the thousands marker. I need to convert them to floats.

a = [['1,200', '4,200'], ['7,000', '-0.03'], [ '5', '0']]
df=pandas.DataFrame(a)

I am guessing I need to use locale.atof. Indeed

df[0].apply(locale.atof)

works as expected. I get a Series of floats.

But when I apply it to the DataFrame, I get an error.

df.apply(locale.atof)

>TypeError: ("cannot convert the series to ", u'occurred at index 0')

and

df[0:1].apply(locale.atof)

gives another error:

>ValueError: ('invalid literal for float(): 1,200', u'occurred at index 0')

So, how do I convert this DataFrame of strings to a DataFrame of floats?

If you're reading in from csv then you can use the thousands arg:

df.read_csv('foo.tsv', sep='\t', thousands=',')

This method is likely to be more efficient than performing the operation as a separate step.

---

You need to set the locale first:

In [ 9]: import locale

In [10]: from locale import atof

In [11]: locale.setlocale(locale.LC_NUMERIC, '')
Out[11]: 'en_GB.UTF-8'

In [12]: df.applymap(atof)
Out[12]:
      0        1
0  1200  4200.00
1  7000    -0.03
2     5     0.00

You can convert one column at a time like this :

df['colname'] = df['colname'].str.replace(',', '').astype(float)

You may use the pandas.Series.str.replace method:

df.iloc[:,:].str.replace(',', '').astype(float)

This method can remove or replace the comma in the string.